论文标题
实时可穿戴步态阶段分割用于跑步和行走
Real-Time Wearable Gait Phase Segmentation For Running And Walking
论文作者
论文摘要
先前的步态相位检测是基于卷积神经网络（CNN）的分类任务，需要繁琐的时间延迟或重叠的滑动窗口的手动设置，以在不同的测试用例下准确地对每个阶段进行准确分类，这不适合流媒体惯性测量单位（IMU）传感器数据，并且无法适应不同的情况。本文仅使用一个六轴IMU传感器提供了基于细分的步态相检测，该传感器可以很容易地以各种速度适应行走和跑步。所提出的分割使用CNN与步态相通知的接受场设置和面向IMU的处理顺序，这可以适合于高于1000Hz的高采样率，以高精度和低采样率降至20Hz，以降至20Hz，以实时计算。在20Hz采样率数据上提出的模型可以达到挥杆时间8.86 ms的平均误差，步态时间为9.12 ms，步态相检测的精度为96.44 \％\％\％\％\％\％\％\％的步步检测准确性。它在手机上的实时实现仅需36毫秒的传感器数据长度。
Previous gait phase detection as convolutional neural network (CNN) based classification task requires cumbersome manual setting of time delay or heavy overlapped sliding windows to accurately classify each phase under different test cases, which is not suitable for streaming Inertial-Measurement-Unit (IMU) sensor data and fails to adapt to different scenarios. This paper presents a segmentation based gait phase detection with only a single six-axis IMU sensor, which can easily adapt to both walking and running at various speeds. The proposed segmentation uses CNN with gait phase aware receptive field setting and IMU oriented processing order, which can fit to high sampling rate of IMU up to 1000Hz for high accuracy and low sampling rate down to 20Hz for real time calculation. The proposed model on the 20Hz sampling rate data can achieve average error of 8.86 ms in swing time, 9.12 ms in stance time and 96.44\% accuracy of gait phase detection and 99.97\% accuracy of stride detection. Its real-time implementation on mobile phone only takes 36 ms for 1 second length of sensor data.
